tensor/grad/concat_test.go

// Copyright (c) 2026 Petr Balvín <opensource@petrbalvin.org> (https://petrbalvin.org)
// SPDX-License-Identifier: MIT

package grad

import (
	"math"
	"testing"

	"sourcedock.dev/petrbalvin/tensor/internal/core"
)

func TestTensorConcatForward(t *testing.T) {
	a, _ := core.FromFloats([]float64{1, 2, 3, 4}, 2, 2)
	b, _ := core.FromFloats([]float64{5, 6, 7, 8, 9, 10}, 2, 3)

	out, err := FromArray(a, false).Concat(FromArray(b, false), 1)
	if err != nil {
		t.Fatal(err)
	}
	if got := out.Data().Shape(); got[0] != 2 || got[1] != 5 {
		t.Fatalf("concat shape: %v", got)
	}
	want := []float64{1, 2, 5, 6, 7, 3, 4, 8, 9, 10}
	for i := range want {
		if g := out.Data().FloatAt(i); g != want[i] {
			t.Fatalf("concat[%d] = %v, want %v", i, g, want[i])
		}
	}

	// Concatenation along the leading axis stacks the blocks.
	c, _ := core.FromFloats([]float64{1, 2}, 1, 2)
	d, _ := core.FromFloats([]float64{3, 4}, 1, 2)
	vert, err := FromArray(c, false).Concat(FromArray(d, false), 0)
	if err != nil {
		t.Fatal(err)
	}
	if vert.Data().Shape()[0] != 2 {
		t.Fatalf("vertical shape: %v", vert.Data().Shape())
	}

	// Mismatched ranks and out-of-range axes error.
	misrank, _ := core.Reshape(c, 2)
	if _, err := FromArray(a, false).Concat(FromArray(misrank, false), 0); err == nil {
		t.Fatal("rank mismatch accepted")
	}
	if _, err := FromArray(a, false).Concat(FromArray(b, false), 2); err == nil {
		t.Fatal("out-of-range dimension accepted")
	}
}

// TestTensorConcatGradients checks both backward spans against central
// differences with a weighted loss so every slot gets a distinct weight.
func TestTensorConcatGradients(t *testing.T) {
	cases := []struct {
		dim            int
		aVal, bVal     []float64
		aShape, bShape []int
		waVal, wbVal   []float64
	}{
		{
			dim:  1,
			aVal: []float64{0.5, -1, 2, 0.25}, aShape: []int{2, 2},
			bVal: []float64{1.5, -0.5, 1, 2, -2, 0.75}, bShape: []int{2, 3},
			waVal: []float64{0.1, -0.4, 0.9, 0.6},
			wbVal: []float64{0.2, 0.3, -0.7, 0.8, 0.05, -0.6},
		},
		{
			dim:  0,
			aVal: []float64{0.3, 1, -0.25, 2}, aShape: []int{2, 2},
			bVal: []float64{-1.5, 0.4, 0.9, 1, -2, 0.7}, bShape: []int{3, 2},
			waVal: []float64{0.55, -0.35, 0.85, 0.15},
			wbVal: []float64{0.45, -0.65, 0.95, 0.05, -0.5, 0.75},
		},
	}
	for _, tc := range cases {
		a, _ := core.FromFloats(tc.aVal, tc.aShape...)
		b, _ := core.FromFloats(tc.bVal, tc.bShape...)
		wa, _ := core.FromFloats(tc.waVal, tc.aShape...)
		wb, _ := core.FromFloats(tc.wbVal, tc.bShape...)

		at := FromArray(a, true)
		bt := FromArray(b, true)
		joint, err := at.Concat(bt, tc.dim)
		if err != nil {
			t.Fatal(err)
		}
		wc, _ := core.Concat(wa, wb, tc.dim)
		scaled, err := joint.Mul(FromArray(wc, false))
		if err != nil {
			t.Fatal(err)
		}
		loss, err := scaled.Sum()
		if err != nil {
			t.Fatal(err)
		}
		if err := loss.Backward(); err != nil {
			t.Fatal(err)
		}

		fa := func(v *core.Array) float64 { return weightedConcatSum(v, b, wa, wb, tc.dim) }
		fb := func(v *core.Array) float64 { return weightedConcatSum(a, v, wa, wb, tc.dim) }
		checkSpan(t, at.Grad(), numericGrad(fa, a))
		checkSpan(t, bt.Grad(), numericGrad(fb, b))
	}
}

// TestTensorConcatGradientDtype keeps each side's gradient in its own
// element type: float32 inputs never come back as float64 leaves.
func TestTensorConcatGradientDtype(t *testing.T) {
	gen := core.NewGenerator(5)
	af, _ := core.Float32s(gen, 6)
	afArr, _ := core.Reshape(af, 2, 3)
	bf, _ := core.Float32s(gen, 6)
	bfArr, _ := core.Reshape(bf, 2, 3)

	at := FromArray(afArr, true)
	bt := FromArray(bfArr, true)
	joint, err := at.Concat(bt, 1)
	if err != nil {
		t.Fatal(err)
	}
	loss, err := joint.Sum()
	if err != nil {
		t.Fatal(err)
	}
	if err := loss.Backward(); err != nil {
		t.Fatal(err)
	}
	if at.Grad().Dtype() != core.Float32 || bt.Grad().Dtype() != core.Float32 {
		t.Fatalf("gradient dtypes: %v and %v", at.Grad().Dtype(), bt.Grad().Dtype())
	}
	for i := range at.Grad().Len() {
		if at.Grad().FloatAt(i) != 1 {
			t.Errorf("float32 gradient slot %d: %v, want 1", i, at.Grad().FloatAt(i))
		}
	}
}

// weightedConcatSum evaluates Σ w∘Concat(x, y, dim) with fixed weights,
// the scalar objective whose gradients the backward is checked against.
func weightedConcatSum(x, y *core.Array, wx, wy *core.Array, dim int) float64 {
	joint, err := FromArray(x, false).Concat(FromArray(y, false), dim)
	if err != nil {
		return math.NaN()
	}
	wc, _ := core.Concat(wx, wy, dim)
	total := 0.0
	for i := range joint.Data().Len() {
		total += wc.FloatAt(i) * joint.Data().FloatAt(i)
	}
	return total
}

// checkSpan reports every slot where the analytic gradient drifts from
// the central-difference reference.
func checkSpan(t *testing.T, got *core.Array, ref []float64) {
	t.Helper()
	if got.Len() != len(ref) {
		t.Fatalf("gradient length %d, reference %d", got.Len(), len(ref))
	}
	for i := range ref {
		if math.Abs(got.FloatAt(i)-ref[i]) > 1e-5 {
			t.Errorf("gradient[%d] = %v, want ≈%v", i, got.FloatAt(i), ref[i])
		}
	}
}